Bibliographic record
Abstract
Tuberculosis is an important issue for nephrologists caring for dialysis patients. Because dialysis patients are immunocompromised, they are at higher risk for reactivation of latent tuberculosis, and they frequently have atypical presentation. Furthermore, hemodialysis units may foster rapid spread of active pulmonary tuberculosis. The diagnosis of active pulmonary tuberculosis still depends on detection of organisms by smear and culture. Newer nucleic acid detection techniques are more sensitive and specific. Nephrologists should remember that nonspecific presentation of tuberculosis including fever, weight loss, and adenopathy are more common in dialysis patients than in the general population, and diagnosis may require biopsy of extrapulmonary tissue. Detection of latent tuberculosis in dialysis patients should only be undertaken if treatment is planned. Generally, this should apply only to potential transplant candidates and younger dialysis patients with longer life expectancy. Tuberculin skin test is very insensitive in dialysis patients, and false-positives occur in patients born in countries where Bacillus Calmette-Guérin vaccine has been used. Blood tests using stimulation of gamma interferon have been shown to be more sensitive tests of latent tuberculosis and may be used in conjunction with tuberculin skin tests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".